{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import path, listdir\n",
    "import re\n",
    "from types import SimpleNamespace as simplenamespace\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atlas_train_all_neighbors\topenTSNEapprox_1000000.log\n",
      "FItSNE_1000000.log\t\topenTSNEapprox_100000.log\n",
      "FItSNE_100000.log\t\topenTSNEapprox_10000.log\n",
      "FItSNE_10000.log\t\topenTSNEapprox_1000.log\n",
      "FItSNE_1000.log\t\t\topenTSNEapprox_250000.log\n",
      "FItSNE_250000.log\t\topenTSNEapprox_500000.log\n",
      "FItSNE_500000.log\t\topenTSNEapprox_5000.log\n",
      "FItSNE_5000.log\t\t\topenTSNEapprox_750000.log\n",
      "FItSNE_750000.log\t\topenTSNEapprox8core_1000000.log\n",
      "FItSNE8core_1000000.log\t\topenTSNEapprox8core_100000.log\n",
      "FItSNE8core_100000.log\t\topenTSNEapprox8core_10000.log\n",
      "FItSNE8core_10000.log\t\topenTSNEapprox8core_1000.log\n",
      "FItSNE8core_1000.log\t\topenTSNEapprox8core_250000.log\n",
      "FItSNE8core_250000.log\t\topenTSNEapprox8core_500000.log\n",
      "FItSNE8core_500000.log\t\topenTSNEapprox8core_5000.log\n",
      "FItSNE8core_5000.log\t\topenTSNEapprox8core_750000.log\n",
      "FItSNE8core_750000.log\t\tsklearn_1000000.log\n",
      "MulticoreTSNE_1000000.log\tsklearn_100000.log\n",
      "MulticoreTSNE_100000.log\tsklearn_10000.log\n",
      "MulticoreTSNE_10000.log\t\tsklearn_1000.log\n",
      "MulticoreTSNE_1000.log\t\tsklearn_250000.log\n",
      "MulticoreTSNE_250000.log\tsklearn_500000.log\n",
      "MulticoreTSNE_500000.log\tsklearn_5000.log\n",
      "MulticoreTSNE_5000.log\t\tsklearn_750000.log\n",
      "MulticoreTSNE_750000.log\tUMAP_1000000.log\n",
      "MulticoreTSNE8core_1000000.log\tUMAP_100000.log\n",
      "MulticoreTSNE8core_100000.log\tUMAP_10000.log\n",
      "MulticoreTSNE8core_10000.log\tUMAP_1000.log\n",
      "MulticoreTSNE8core_1000.log\tUMAP_250000.log\n",
      "MulticoreTSNE8core_250000.log\tUMAP_500000.log\n",
      "MulticoreTSNE8core_500000.log\tUMAP_5000.log\n",
      "MulticoreTSNE8core_5000.log\tUMAP_750000.log\n",
      "MulticoreTSNE8core_750000.log\n"
     ]
    }
   ],
   "source": [
    "!ls logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG_DIR = path.join(path.abspath(\".\"), \"logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_points = [1000, 5000, 10_000, 100_000, 250_000, 500_000, 750_000, 1_000_000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_regex_func(pattern, fname):\n",
    "    \n",
    "    def _wrapper(n_points):\n",
    "        matches = [re.findall(pattern, line) for line in\n",
    "               open(path.join(LOG_DIR, f\"{fname}_{n_points}.log\"))]\n",
    "        matches = list(filter(len, matches))\n",
    "        matches = list(map(lambda x: float(x[0]), matches))\n",
    "\n",
    "        return np.array(matches)\n",
    "    \n",
    "    return _wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[228.33709931 225.79553986 226.98500681]\n"
     ]
    }
   ],
   "source": [
    "parse_opentsne = make_regex_func(r\"openTSNE: Full (\\d+\\.\\d+)\", \"openTSNEapprox\")\n",
    "parse_opentsne_nn = make_regex_func(r\"openTSNE: NN search (\\d+\\.\\d+)\", \"openTSNEapprox\")\n",
    "parse_opentsne_optimization = make_regex_func(r\"openTSNE: Optimization (\\d+\\.\\d+)\", \"openTSNEapprox\")\n",
    "\n",
    "opentsne = simplenamespace(full=parse_opentsne, nn=parse_opentsne_nn, optim=parse_opentsne_optimization)\n",
    "    \n",
    "print(opentsne.full(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[104.89250898 104.21664405 104.45669413]\n"
     ]
    }
   ],
   "source": [
    "parse_opentsne8 = make_regex_func(r\"openTSNE: Full (\\d+\\.\\d+)\", \"openTSNEapprox8core\")\n",
    "parse_opentsne_nn8 = make_regex_func(r\"openTSNE: NN search (\\d+\\.\\d+)\", \"openTSNEapprox8core\")\n",
    "parse_opentsne_optimization8 = make_regex_func(r\"openTSNE: Optimization (\\d+\\.\\d+)\", \"openTSNEapprox8core\")\n",
    "\n",
    "opentsne8 = simplenamespace(full=parse_opentsne8, nn=parse_opentsne_nn8, optim=parse_opentsne_optimization8)\n",
    "    \n",
    "print(opentsne8.full(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[177.11653399 176.98441291 177.97437263]\n"
     ]
    }
   ],
   "source": [
    "parse_fitsne = make_regex_func(r\"FIt-SNE: (\\d+\\.\\d+)\", \"FItSNE\")\n",
    "parse_fitsne_nn = make_regex_func(r\"100\\% (\\d+\\.\\d+)\", \"FItSNE\")\n",
    "\n",
    "def parse_fitsne_optimization(n_points):\n",
    "    full_times = parse_fitsne(n_points)\n",
    "    nn_times = parse_fitsne_nn(n_points)\n",
    "    return full_times - nn_times\n",
    "\n",
    "fitsne = simplenamespace(full=parse_fitsne, nn=parse_fitsne_nn, optim=parse_fitsne_optimization)\n",
    "\n",
    "print(fitsne.full(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.23060322 13.45884466 12.84704351]\n"
     ]
    }
   ],
   "source": [
    "parse_fitsne8 = make_regex_func(r\"FIt-SNE: (\\d+\\.\\d+)\", \"FItSNE8core\")\n",
    "parse_fitsne_nn8 = make_regex_func(r\"100\\% (\\d+\\.\\d+)\", \"FItSNE8core\")\n",
    "\n",
    "def parse_fitsne_optimization8(n_points):\n",
    "    full_times = parse_fitsne8(n_points)\n",
    "    nn_times = parse_fitsne_nn8(n_points)\n",
    "    return full_times - nn_times\n",
    "\n",
    "fitsne8 = simplenamespace(full=parse_fitsne8, nn=parse_fitsne_nn8, optim=parse_fitsne_optimization8)\n",
    "\n",
    "print(fitsne8.full(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[953.92509246 936.90797281 933.63834786]\n"
     ]
    }
   ],
   "source": [
    "parse_multicore = make_regex_func(r\"Multicore t-SNE: (\\d+\\.\\d+)\", \"MulticoreTSNE\")\n",
    "parse_multicore_nn = make_regex_func(r\"Done in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE\")\n",
    "parse_multicore_optimization = make_regex_func(r\"Fitting performed in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE\")\n",
    "\n",
    "multicore = simplenamespace(full=parse_multicore, nn=parse_multicore_nn, optim=parse_multicore_optimization)\n",
    "\n",
    "print(multicore.full(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.9090538  2.66580534 2.82796788]\n"
     ]
    }
   ],
   "source": [
    "parse_multicore8 = make_regex_func(r\"Multicore t-SNE: (\\d+\\.\\d+)\", \"MulticoreTSNE8core\")\n",
    "parse_multicore_nn8 = make_regex_func(r\"Done in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE8core\")\n",
    "parse_multicore_optimization8 = make_regex_func(r\"Fitting performed in (\\d+\\.\\d+) seconds\", \"MulticoreTSNE8core\")\n",
    "\n",
    "multicore8 = simplenamespace(full=parse_multicore8, nn=parse_multicore_nn8, optim=parse_multicore_optimization8)\n",
    "\n",
    "print(multicore8.full(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[65.73254728 66.63277197 67.05821729]\n"
     ]
    }
   ],
   "source": [
    "parse_sklearn = make_regex_func(r\"scikit-learn t-SNE: (\\d+\\.\\d+)\", \"sklearn\")\n",
    "parse_sklearn_nn = make_regex_func(r\"neighbors for .* samples in (\\d+\\.\\d+)s\", \"sklearn\")\n",
    "\n",
    "def parse_sklearn_optimization(n_points):\n",
    "    full_times = parse_sklearn(n_points)\n",
    "    nn_times = parse_sklearn_nn(n_points)\n",
    "    return full_times - nn_times\n",
    "\n",
    "sklearn = simplenamespace(full=parse_sklearn, nn=parse_sklearn_nn, optim=parse_sklearn_optimization)\n",
    "\n",
    "print(sklearn.full(10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25.80454087 21.35700297 21.26818299]\n"
     ]
    }
   ],
   "source": [
    "parse_umap = make_regex_func(r\"UMAP: (\\d+\\.\\d+)\", \"UMAP\")\n",
    "\n",
    "umap = simplenamespace(full=parse_umap, nn=lambda *a, **kw: ..., optim=lambda *a, **kw: ...)\n",
    "\n",
    "print(umap.full(10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ppolicar/.local/lib/python3.7/site-packages/pandas/core/frame.py:6211: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "df = pd.DataFrame(columns=[\"time\", \"nn_time\", \"optim_time\", \"n_samples\", \"method\"])\n",
    "\n",
    "\n",
    "for method_name, method in [(\"openTSNE (1 core)\", opentsne),\n",
    "                            (\"openTSNE (8 cores)\", opentsne8),\n",
    "                            (\"MulticoreTSNE (1 core)\", multicore),\n",
    "                            (\"MulticoreTSNE (8 cores)\", multicore8),\n",
    "                            (\"FIt-SNE (1 core)\", fitsne),\n",
    "                            (\"FIt-SNE (8 cores)\", fitsne8),\n",
    "                            (\"scikit-learn (1 core)\", sklearn),\n",
    "                            (\"UMAP (1 core)\", umap)]:\n",
    "    for n in n_points:\n",
    "        try:\n",
    "            tmp_df = pd.DataFrame({\n",
    "                \"time\": method.full(n),\n",
    "                #\"nn_time\": method.nn(n),\n",
    "                #\"optim_time\": method.optim(n),\n",
    "            })\n",
    "            tmp_df[\"n_samples\"] = n\n",
    "            tmp_df[\"method\"] = method_name\n",
    "            \n",
    "            df = df.append(tmp_df, ignore_index=True)\n",
    "            \n",
    "        except FileNotFoundError as e:\n",
    "            warnings.warn(str(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>n_samples</th>\n",
       "      <th>nn_time</th>\n",
       "      <th>optim_time</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.150531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.151521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.724179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.766439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.733601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              method n_samples nn_time optim_time       time\n",
       "0  openTSNE (1 core)      1000     NaN        NaN  15.150531\n",
       "1  openTSNE (1 core)      1000     NaN        NaN  28.151521\n",
       "2  openTSNE (1 core)      1000     NaN        NaN  21.724179\n",
       "3  openTSNE (1 core)      5000     NaN        NaN  30.766439\n",
       "4  openTSNE (1 core)      5000     NaN        NaN  25.733601"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>nn_time</th>\n",
       "      <th>optim_time</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th>n_samples</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">FIt-SNE (1 core)</th>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">FIt-SNE (8 cores)</th>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">MulticoreTSNE (1 core)</th>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">MulticoreTSNE (8 cores)</th>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">UMAP (1 core)</th>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">openTSNE (1 core)</th>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">openTSNE (8 cores)</th>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">scikit-learn (1 core)</th>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   nn_time  optim_time  time\n",
       "method                  n_samples                           \n",
       "FIt-SNE (1 core)        1000             0           0     3\n",
       "                        5000             0           0     3\n",
       "                        10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "                        750000           0           0     3\n",
       "                        1000000          0           0     3\n",
       "FIt-SNE (8 cores)       1000             0           0     3\n",
       "                        5000             0           0     3\n",
       "                        10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "                        750000           0           0     3\n",
       "                        1000000          0           0     3\n",
       "MulticoreTSNE (1 core)  1000             0           0     3\n",
       "                        5000             0           0     3\n",
       "                        10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "                        750000           0           0     3\n",
       "                        1000000          0           0     3\n",
       "MulticoreTSNE (8 cores) 1000             0           0     3\n",
       "                        5000             0           0     3\n",
       "                        10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "...                                    ...         ...   ...\n",
       "UMAP (1 core)           10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "                        750000           0           0     3\n",
       "                        1000000          0           0     3\n",
       "openTSNE (1 core)       1000             0           0     3\n",
       "                        5000             0           0     3\n",
       "                        10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "                        750000           0           0     3\n",
       "                        1000000          0           0     3\n",
       "openTSNE (8 cores)      1000             0           0     3\n",
       "                        5000             0           0     3\n",
       "                        10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "                        750000           0           0     3\n",
       "                        1000000          0           0     3\n",
       "scikit-learn (1 core)   1000             0           0     3\n",
       "                        5000             0           0     3\n",
       "                        10000            0           0     3\n",
       "                        100000           0           0     3\n",
       "                        250000           0           0     3\n",
       "                        500000           0           0     3\n",
       "                        750000           0           0     3\n",
       "                        1000000          0           0     3\n",
       "\n",
       "[64 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"method\", \"n_samples\"]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "#sns.set(\"notebook\", \"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>n_samples</th>\n",
       "      <th>nn_time</th>\n",
       "      <th>optim_time</th>\n",
       "      <th>time</th>\n",
       "      <th>time_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.150531</td>\n",
       "      <td>0.252509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.151521</td>\n",
       "      <td>0.469192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.724179</td>\n",
       "      <td>0.362070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.766439</td>\n",
       "      <td>0.512774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>openTSNE (1 core)</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.733601</td>\n",
       "      <td>0.428893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              method n_samples nn_time optim_time       time  time_min\n",
       "0  openTSNE (1 core)      1000     NaN        NaN  15.150531  0.252509\n",
       "1  openTSNE (1 core)      1000     NaN        NaN  28.151521  0.469192\n",
       "2  openTSNE (1 core)      1000     NaN        NaN  21.724179  0.362070\n",
       "3  openTSNE (1 core)      5000     NaN        NaN  30.766439  0.512774\n",
       "4  openTSNE (1 core)      5000     NaN        NaN  25.733601  0.428893"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"time_min\"] = df[\"time\"] / 60\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "#g = sns.lineplot(x=\"n_samples\", y=\"time_min\", hue=\"method\", data=df, ax=ax)\n",
    "\n",
    "sns.despine(offset=20)\n",
    "\n",
    "ax.set_title(\"t-SNE implementation benchmarks\", loc=\"Left\", fontdict={\n",
    "    \"fontsize\": \"13\"\n",
    "})\n",
    "ax.set_xlabel(\"$n$ samples\")\n",
    "ax.set_ylabel(\"Time (in minutes)\")\n",
    "\n",
    "ax.grid(color=\"0.9\", linestyle=\"--\", linewidth=1)\n",
    "\n",
    "# Lines\n",
    "d = df.groupby([\"method\", \"n_samples\"]).mean().reset_index()\n",
    "d_std = df.groupby([\"method\", \"n_samples\"]).std().reset_index()\n",
    "\n",
    "# openTSNE\n",
    "which = \"openTSNE (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n",
    "\n",
    "which = \"openTSNE (8 cores)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", linestyle=\"dashed\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n",
    "\n",
    "# FIt-SNE\n",
    "which = \"FIt-SNE (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n",
    "\n",
    "which = \"FIt-SNE (8 cores)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", linestyle=\"dashed\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n",
    "\n",
    "# MulticoreTSNE\n",
    "which = \"MulticoreTSNE (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n",
    "\n",
    "which = \"MulticoreTSNE (8 cores)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", linestyle=\"dashed\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n",
    "\n",
    "# sklearn\n",
    "which = \"scikit-learn (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#C44E52\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#C44E52\")\n",
    "\n",
    "ax.set_xlim(0, 1_000_000)\n",
    "ax.set_ylim(0, 130)\n",
    "ax.set_yticks(range(0, 130, 15))\n",
    "\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(\n",
    "    lambda x, p: format(int(x), ',').replace(\",\", \".\")))\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(frameon=False, loc='upper right')\n",
    "\n",
    "plt.savefig(\"benchmarks.png\", dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1f88d67550>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "#g = sns.lineplot(x=\"n_samples\", y=\"time_min\", hue=\"method\", data=df, ax=ax)\n",
    "\n",
    "sns.despine(offset=20)\n",
    "\n",
    "ax.set_title(\"t-SNE implementation benchmarks\", loc=\"Left\", fontdict={\n",
    "    \"fontsize\": \"13\"\n",
    "})\n",
    "ax.set_xlabel(\"$n$ samples\")\n",
    "ax.set_ylabel(\"Time (in minutes)\")\n",
    "\n",
    "ax.grid(color=\"0.9\", linestyle=\"--\", linewidth=1)\n",
    "\n",
    "# Lines\n",
    "d = df.groupby([\"method\", \"n_samples\"]).mean().reset_index()\n",
    "d_std = df.groupby([\"method\", \"n_samples\"]).std().reset_index()\n",
    "\n",
    "# openTSNE\n",
    "which = \"openTSNE (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n",
    "\n",
    "which = \"openTSNE (8 cores)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#4C72B0\", linestyle=\"dashed\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#4C72B0\")\n",
    "\n",
    "# FIt-SNE\n",
    "which = \"FIt-SNE (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n",
    "\n",
    "which = \"FIt-SNE (8 cores)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#DD8452\", linestyle=\"dashed\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#DD8452\")\n",
    "\n",
    "# MulticoreTSNE\n",
    "which = \"MulticoreTSNE (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n",
    "\n",
    "which = \"MulticoreTSNE (8 cores)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#55A868\", linestyle=\"dashed\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#55A868\")\n",
    "\n",
    "# sklearn\n",
    "#which = \"scikit-learn (1 core)\"\n",
    "#tmp = d[d[\"method\"] == which]\n",
    "#tmp1 = d_std[d_std[\"method\"] == which]\n",
    "#ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#C44E52\", label=which)\n",
    "#ax.fill_between(tmp1[\"n_samples\"],\n",
    "#                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "#                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#C44E52\")\n",
    "\n",
    "# UMAP\n",
    "which = \"UMAP (1 core)\"\n",
    "tmp = d[d[\"method\"] == which]\n",
    "tmp1 = d_std[d_std[\"method\"] == which]\n",
    "ax.plot(tmp[\"n_samples\"], tmp[\"time_min\"], c=\"#C44E52\", label=which)\n",
    "ax.fill_between(tmp1[\"n_samples\"],\n",
    "                tmp[\"time_min\"] + tmp1[\"time_min\"],\n",
    "                tmp[\"time_min\"] - tmp1[\"time_min\"], alpha=0.25, color=\"#C44E52\")\n",
    "\n",
    "ax.set_xlim(0, 1_000_000)\n",
    "ax.set_ylim(0, 130)\n",
    "ax.set_yticks(range(0, 130, 15))\n",
    "\n",
    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(\n",
    "    lambda x, p: format(int(x), ',').replace(\",\", \".\")))\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(frameon=False, loc='upper right')\n",
    "\n",
    "#plt.savefig(\"benchmarks.png\", dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
